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Why Use Imagery or Raster Data?

When people think about geospatial imagery, they often first think of the worldwide collections of satellite imagery and aerial photography in Google Earth, Bing Maps, and ArcGIS Online. These are large collections georeferenced by a process called orthorectification and mosaicked into tiles optimized for Web distribution. They are ideal for basemaps to add context to maps and geospatial services. However, this is only one of the many benefits of using imagery in a GIS.

Esri uses the word imagery to generically refer to all forms of raster data, which is data that is organized into pixels (or cells) of rows and columns (a grid). Each cell contains values of geospatial information. Imagery is one form of raster data where each pixel stores spectral data that was collected by satellites or as aerial photographs. The pixels store either the color, wavelength (band), or intensity of a predetermined wavelength or wavelengths. For example, the pixels in most black-and-white aerial photography store the intensity of visible light. Other forms of raster data include temperature, elevation, land-use, and soil datasets. Satellites and digital aerial cameras often contain many sensors, which allow them to record the intensity of many different bands simultaneously. This is called multispectral imagery. Just like a television or a computer monitor, the bands of multispectral imagery can be combined to produce full-color images, so much more information can be revealed from imagery simply by creating and comparing different band combinations. For example, combining infrared, red, and green bands produces a "false color" image that displays healthy vegetation in bright red.

Imagery is an inherently temporal dataset. By comparing multiple images for a location that were taken hours, days, years, or even decades apart, it is easy to see how things have changed and evolved over time. The ability of ArcGIS to quickly process and disseminate imagery means that it's possible to have near real-time information.

Raster data includes any type of gridded dataset. In addition to imagery, there are land surface elevations (digital elevation models), temperature heat maps (such as in the local newspaper), lidar data, scanned maps, and thematic maps. An example of a thematic map is a vegetation map where each pixel represents a different classification, such as agriculture, bare ground, water, wetlands, forest, desert, and urban. Raster data can also be used to store and display the results of a spatial analysis. The ArcGIS Spatial Analyst extension heavily uses the raster format when performing its analyses, whether it is performing an image classification, using a digital elevation model for groundwater contaminant analysis, or generating a raster heat map from a vector dataset.

Imagery and raster data are critical for a complete GIS system. They not only provide contextual basemaps for other geospatial datasets but also provide and reveal actionable information that improves decision making.